This document describes how to go from raw vcftools output of diversity metrics (Fst, pi, and Tajima’s D) to Manhattan plots, including making figures for the manuscript associated with this repo. This script is a result of brute forcing things to work: I am certainly no expert, and thus lots of the notes refer to my own dumb mistakes.

library(tidyverse)
library(qqman)
library(scales)

Prepare files: join vcftools output into a single table.

First, read in raw output files from vcftools (in this repo, see filter-scan.sh for code to generate).

fst.UKUS.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseud_nostep_UKUS_50kb.windowed.weir.fst",sep="\t"))
fst.AUUK.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseud_nostep_AUUK_50kb.windowed.weir.fst",sep="\t"))
fst.USAU.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseud_nostep_AUUS_50kb.windowed.weir.fst",sep="\t"))
pi.UK.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_UK_pi_50kb.windowed.pi",sep="\t"))
pi.AU.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_AU_pi_50kb.windowed.pi",sep="\t"))
pi.US.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_US_pi_50kb.windowed.pi",sep="\t"))
TajD.UK.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_UK_TajimaD_50kb.Tajima.D",sep="\t"))
TajD.AU.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_AU_TajimaD_50kb.Tajima.D",sep="\t"))
TajD.US.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_US_TajimaD_50kb.Tajima.D",sep="\t"))

vcftools outputs a few identifies for position: “CHROM,” “BIN_START” and “BIN_END” for .fst and .pi files, but only “CHROM” AND “BIN_START” for .Tajima.D files. Unfortunately, the numbering is off, so we’ll add 1 to every “BIN_START” in the .Tajima.D files.

head(TajD.AU.50kb)
## # A tibble: 6 x 4
##   CHROM BIN_START N_SNPS TajimaD
##   <fct>     <int>  <int>   <dbl>
## 1 10            0    147  0.583 
## 2 10        50000    334  0.372 
## 3 10       100000    301  0.836 
## 4 10       150000     98  1.56  
## 5 10       200000    102 -0.557 
## 6 10       250000    139 -0.0356
head(fst.AUUK.50kb)
## # A tibble: 6 x 6
##   CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST  MEAN_FST
##   <fct>     <int>   <int>      <int>        <dbl>     <dbl>
## 1 10            1   50000        164      0.00792  0.0105  
## 2 10        50001  100000        379      0.0595   0.0500  
## 3 10       100001  150000        311      0.00728 -0.000459
## 4 10       150001  200000        120      0.0162   0.0275  
## 5 10       200001  250000        121      0.126    0.0926  
## 6 10       250001  300000        155      0.109    0.0747
TajD.UK.50kb$BIN_START <- TajD.UK.50kb$BIN_START + 1
TajD.AU.50kb$BIN_START <- TajD.AU.50kb$BIN_START + 1
TajD.US.50kb$BIN_START <- TajD.US.50kb$BIN_START + 1

Now BIN_START should match. To use dplyr, we’ll need a column that specifes a unique position in the genome. Create a new column that joins “CHROM” and “BIN_START” so that we match each value based on actual position in the genome.

fst.UKUS.50kb$POS_ID <- paste(fst.UKUS.50kb$CHROM,fst.UKUS.50kb$BIN_START,sep="-")
fst.AUUK.50kb$POS_ID <- paste(fst.AUUK.50kb$CHROM,fst.AUUK.50kb$BIN_START,sep="-")
fst.USAU.50kb$POS_ID <- paste(fst.USAU.50kb$CHROM,fst.USAU.50kb$BIN_START,sep="-")
pi.UK.50kb$POS_ID <- paste(pi.UK.50kb$CHROM,pi.UK.50kb$BIN_START,sep="-")
pi.AU.50kb$POS_ID <- paste(pi.AU.50kb$CHROM,pi.AU.50kb$BIN_START,sep="-")
pi.US.50kb$POS_ID <- paste(pi.US.50kb$CHROM,pi.US.50kb$BIN_START,sep="-")
TajD.UK.50kb$POS_ID <- paste(TajD.UK.50kb$CHROM,TajD.UK.50kb$BIN_START,sep="-")
TajD.AU.50kb$POS_ID <- paste(TajD.AU.50kb$CHROM,TajD.AU.50kb$BIN_START,sep="-")
TajD.US.50kb$POS_ID <- paste(TajD.US.50kb$CHROM,TajD.US.50kb$BIN_START,sep="-")

Now drop column names that we no longer need, otherwise we’ll have a giant table after all the merging.

fst.AUUK.50kb <- fst.AUUK.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
fst.USAU.50kb <- fst.USAU.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
pi.UK.50kb <- pi.UK.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
pi.AU.50kb <- pi.AU.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
pi.US.50kb <- pi.US.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
TajD.UK.50kb <- TajD.UK.50kb %>% select(-CHROM, -BIN_START, -N_SNPS)
TajD.AU.50kb <- TajD.AU.50kb %>% select(-CHROM, -BIN_START, -N_SNPS)
TajD.US.50kb <- TajD.US.50kb %>% select(-CHROM, -BIN_START, -N_SNPS)

We’ll join tables based on the unique position identified (POS_ID). The rename() command ensures that each new column header specifies population (new name first). Heads up: each time you join two tables, those two tables are no longer accessible except as a joined table!

fstUKUS.fstAUUK.50kb <- left_join(fst.UKUS.50kb,fst.AUUK.50kb, by = "POS_ID", copy = FALSE, suffix=c("_UKUS","_AUUK"))
fst.50kb <- left_join(fstUKUS.fstAUUK.50kb,fst.USAU.50kb, by = "POS_ID", copy = FALSE, suffix=c("","_USAU"))
fst.50kb <- rename(fst.50kb, WEIGHTED_FST_USAU = WEIGHTED_FST)
fst.50kb <- rename(fst.50kb, MEAN_FST_USAU = MEAN_FST)
fst.50kb.piUK <- left_join(fst.50kb,pi.UK.50kb, by = "POS_ID", copy = FALSE)
fst.50kb.piUK.piUS <- left_join(fst.50kb.piUK,pi.US.50kb, by = "POS_ID", copy = FALSE,suffix=c("_UK","_US"))
fst.pi.50kb <- left_join(fst.50kb.piUK.piUS,pi.AU.50kb, by = "POS_ID", copy = FALSE)
fst.pi.50kb <- rename(fst.pi.50kb, PI_AU = PI)
fst.pi.50kb.TajDUK <- left_join(fst.pi.50kb,TajD.UK.50kb, by = "POS_ID", copy = FALSE)
fst.pi.50kb.TajDUK.TajDUS <- left_join(fst.pi.50kb.TajDUK,TajD.US.50kb, by = "POS_ID", copy = FALSE, suffix=c("_UK","_US"))
div <- left_join(fst.pi.50kb.TajDUK.TajDUS,TajD.AU.50kb, by = "POS_ID", copy = FALSE)
div <- rename(div, TajimaD_AU = TajimaD)

We’re going to drop data from: * small scaffolds (which conveniently start with ‘KQ’ or ‘LNCF’, * any rows (positions) with missing data (NA), * and also coerce “negative” FST or pi to zero. We replace POS_ID (which was converted to numeric) with SNP. For the qqman package, chromosomes need to be a numeric value, so we also use lapply() below to rename chromosomes.

div <- filter(div, !grepl('KQ',CHROM))
div <- filter(div, !grepl('LNCF',CHROM))
div <- filter(div, !grepl('Unknown',CHROM))
div <- div %>% drop_na()
div[,c(5:6)][div[,c(5:6)] < 0] <- 0
div[,c(8:14)][div[,c(8:14)] < 0] <- 0
div <- data.frame(lapply(div, function(x) {gsub("1A", "1.25", x)}))
div <- data.frame(lapply(div, function(x) {gsub("1B", "1.75", x)}))
div <- data.frame(lapply(div, function(x) {gsub("4A", "4.5", x)}))
div <- data.frame(lapply(div, function(x) {gsub("LG5", "28", x)}))
div <- data.frame(lapply(div, function(x) {gsub("LGE22", "29", x)}))
div <- data.frame(lapply(div, function(x) {gsub("Z", "0", x)}))
indx <- sapply(div, is.factor)
div[indx] <- lapply(div[indx], function(x) as.numeric(as.character(x)))
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
div <- div %>% select(-POS_ID)
div$SNP <- seq.int(nrow(div))
str(div)
## 'data.frame':    20071 obs. of  17 variables:
##  $ CHROM            : num  10 10 10 10 10 10 10 10 10 10 ...
##  $ BIN_START        : num  1 50001 100001 150001 200001 ...
##  $ BIN_END          : num  50000 100000 150000 200000 250000 300000 350000 400000 450000 500000 ...
##  $ N_VARIANTS       : num  165 377 311 120 121 156 80 101 24 18 ...
##  $ WEIGHTED_FST_UKUS: num  0.01745 0.03751 0 0 0.00586 ...
##  $ MEAN_FST_UKUS    : num  0.0133 0.0272 0 0 0 ...
##  $ WEIGHTED_FST_AUUK: num  0.00792 0.05948 0.00728 0.01624 0.12564 ...
##  $ MEAN_FST_AUUK    : num  0.0105 0.05 0 0.0275 0.0926 ...
##  $ WEIGHTED_FST_USAU: num  0.01291 0.00258 0 0 0.00315 ...
##  $ MEAN_FST_USAU    : num  0.0093 0.00585 0 0 0 ...
##  $ PI_UK            : num  0.001053 0.002189 0.002212 0.000915 0.000879 ...
##  $ PI_US            : num  0.001063 0.002373 0.002003 0.000829 0.0007 ...
##  $ PI_AU            : num  0.001024 0.002211 0.0022 0.000804 0.000532 ...
##  $ TajimaD_UK       : num  0.808 0.293 0.877 1.426 0.968 ...
##  $ TajimaD_US       : num  0.42348 0.58012 0.80239 1.05163 -0.00424 ...
##  $ TajimaD_AU       : num  0.583 0.372 0.836 1.557 -0.557 ...
##  $ SNP              : int  1 2 3 4 5 6 7 8 9 10 ...

Now we’re ready to plot and calculate genome-wide values!

Exploring genetic variation

First, we look at the distribution of variation across the genome.

Manhattan plots

manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.UKUS.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()
## quartz_off_screen 
##                 2
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.AUUK.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()
## quartz_off_screen 
##                 2
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_USAU", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.USAU.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_USAU", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()
## quartz_off_screen 
##                 2

Identifying outliers

quantile(div$WEIGHTED_FST_AUUK, c(.9,.95,.99,.999)) 
##       90%       95%       99%     99.9% 
## 0.0676462 0.0862444 0.1490122 0.3077638
quantile(div$WEIGHTED_FST_UKUS, c(.9,.95,.99,.999)) 
##       90%       95%       99%     99.9% 
## 0.0441469 0.0609766 0.1156152 0.2228447
mean(div$WEIGHTED_FST_AUUK) + 5*sd(div$WEIGHTED_FST_AUUK)
## [1] 0.1936444
mean(div$WEIGHTED_FST_UKUS) + 5*sd(div$WEIGHTED_FST_UKUS)
## [1] 0.1406712
div.outliers.AUUK <- div[which(div$WEIGHTED_FST_AUUK > quantile(div$WEIGHTED_FST_AUUK,.99)),]
div.outliers.USUK <- div[which(div$WEIGHTED_FST_UKUS > quantile(div$WEIGHTED_FST_UKUS,.99)),]

div.hifst.AUUK <- div[which(div$WEIGHTED_FST_AUUK > 0.1),]
div.hifst.UKUS <- div[which(div$WEIGHTED_FST_UKUS > 0.1),]

unique(div.outliers.USUK$CHROM)
##  [1] 11.00 12.00 13.00 17.00 19.00  1.25  1.00 28.00  2.00  3.00  4.50  4.00
## [13]  6.00 29.00  0.00
length(div.outliers.USUK$SNP)
## [1] 201
write.csv(div.outliers.USUK,"/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/FstOutliers.USUK.csv")

unique(div.outliers.AUUK$CHROM)
##  [1] 10.00 12.00 13.00 17.00 18.00  1.25  1.00 23.00 27.00  2.00  3.00  4.50
## [13]  4.00  5.00  6.00  7.00  8.00  0.00
length(div.outliers.AUUK$SNP)
## [1] 201
write.csv(div.outliers.AUUK,"/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/FstOutliers.AUUK.csv")

What’s going on w/ other metrics at these outliers?

summary(div.outliers.AUUK$PI_UK)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000025 0.0000730 0.0001890 0.0009734 0.0014188 0.0057224
summary(div.outliers.AUUK$PI_AU)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 1.383e-05 7.067e-05 2.285e-04 9.324e-04 1.447e-03 5.313e-03
summary(div.outliers.AUUK$TajimaD_AU)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -2.3050  0.2574  0.7501  0.6479  1.2528  2.6922
summary(div.outliers.AUUK$TajimaD_UK)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -1.2937  0.1481  0.6493  0.5926  1.0098  2.3588
summary(div.outliers.USUK$PI_US)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 7.167e-06 6.250e-05 2.890e-04 8.204e-04 1.281e-03 5.202e-03
summary(div.outliers.USUK$PI_UK)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000210 0.0000765 0.0002912 0.0008650 0.0012335 0.0058409
summary(div.outliers.USUK$TajimaD_US)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -2.1488 -0.4076  0.4343  0.3082  0.9422  2.9728
summary(div.outliers.USUK$TajimaD_UK)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -1.9252  0.3047  0.8963  0.8645  1.4612  2.5895
div.outliers.AUUK.lowFstUSAU <- div.outliers.AUUK[which(div.outliers.AUUK$WEIGHTED_FST_USAU < 0.01 ),] 
div.outliers.AUUK.lowFstUSAU
##       CHROM BIN_START  BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 7     10.00    300001   350000         80         0.1027220     0.0600280
## 9     10.00    400001   450000         24         0.0578802     0.0408439
## 10    10.00    450001   500000         18         0.0641170     0.0438801
## 903   12.00   3500001  3550000        313         0.1841560     0.1390400
## 1414  13.00   8300001  8350000          6         0.0888562     0.0764819
## 2251  17.00   2350001  2400000         90         0.0718635     0.0453814
## 3399   1.25  25150001 25200000         80         0.1291380     0.1210420
## 3400   1.25  25200001 25250000         65         0.0763648     0.0755731
## 3793   1.25  44850001 44900000          7         0.1502550     0.1512780
## 3946   1.25  52500001 52550000          9         0.1002160     0.0841356
## 13219  4.50   6100001  6150000        132         0.1381170     0.1304820
## 13220  4.50   6150001  6200000         84         0.1382960     0.1325750
## 16265  6.00   5500001  5550000         61         0.2868590     0.2827380
## 16267  6.00   5600001  5650000         66         0.2488270     0.2538260
## 16268  6.00   5650001  5700000         90         0.2626990     0.2607650
## 16271  6.00   5800001  5850000         95         0.1569920     0.1609480
## 19739  0.00  51900001 51950000         60         0.1874130     0.1492570
## 19962  0.00  63050001 63100000        232         0.1055780     0.0686902
## 19999  0.00  64900001 64950000        186         0.1765470     0.1102810
## 20000  0.00  64950001 65000000        240         0.2637760     0.1947360
## 20002  0.00  65050001 65100000        450         0.1136620     0.0975800
##       WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU
## 7              0.190274      0.122576       0.000000000   0.000000000
## 9              0.150496      0.109492       0.000000000   0.000000000
## 10             0.197232      0.155583       0.000000000   0.000000000
## 903            0.172857      0.132176       0.000000000   0.000000000
## 1414           0.165927      0.125393       0.004134130   0.019587600
## 2251           0.179935      0.149568       0.000000000   0.000475237
## 3399           0.187165      0.173330       0.000000000   0.000000000
## 3400           0.179424      0.168180       0.000000000   0.000000000
## 3793           0.192503      0.166546       0.008088430   0.000000000
## 3946           0.157895      0.133751       0.000000000   0.000000000
## 13219          0.149469      0.141418       0.000000000   0.000000000
## 13220          0.164826      0.153364       0.000000000   0.000000000
## 16265          0.178806      0.178051       0.000000000   0.000000000
## 16267          0.155095      0.162605       0.000000000   0.000000000
## 16268          0.172276      0.167504       0.000000000   0.010494800
## 16271          0.155570      0.151752       0.004985280   0.006458370
## 19739          0.158887      0.126400       0.006943530   0.004539620
## 19962          0.159585      0.111195       0.000000000   0.003472230
## 19999          0.250744      0.187151       0.004209500   0.002449910
## 20000          0.230248      0.162655       0.000000000   0.000000000
## 20002          0.171135      0.133933       0.000369775   0.000000000
##             PI_UK       PI_US       PI_AU TajimaD_UK TajimaD_US TajimaD_AU
## 7     5.80833e-04 3.92847e-04 2.90669e-04   1.010540  -0.569214  -1.333590
## 9     1.45667e-04 1.08668e-04 7.26667e-05   0.674272  -0.938436  -1.463810
## 10    1.15667e-04 7.03333e-05 3.06667e-05   0.510990  -1.402140  -0.520315
## 903   1.58583e-03 2.29027e-03 2.38120e-03   0.322469   1.214250   1.230810
## 1414  3.53333e-05 4.86667e-05 5.13333e-05  -0.078611   2.026320   1.435800
## 2251  5.73500e-04 5.66689e-04 5.08838e-04   0.877600   0.367470   0.645267
## 3399  6.12500e-04 8.16000e-04 7.46855e-04   1.160520   2.972800   2.474060
## 3400  4.95167e-04 6.38006e-04 5.69339e-04   1.386520   2.737810   2.391710
## 3793  6.46667e-05 4.11669e-05 4.38335e-05   1.877030   0.118964   0.239407
## 3946  7.31667e-05 6.43336e-05 5.60000e-05   1.281030   0.765878   0.581803
## 13219 1.16983e-03 4.09190e-04 3.88846e-04   2.126910  -2.148800  -2.179840
## 13220 7.55674e-04 2.66003e-04 2.38502e-04   2.154800  -2.022640  -2.304990
## 16265 2.88333e-05 5.33346e-04 4.75333e-04   1.723710   2.056500   1.339670
## 16267 5.08333e-05 5.92009e-04 5.25841e-04   0.721746   2.161640   1.510490
## 16268 6.93333e-05 7.88034e-04 6.67837e-04  -1.293670   2.136270   1.375800
## 16271 1.58333e-04 6.64365e-04 7.56515e-04  -0.174387   1.129640   1.427160
## 19739 3.98671e-04 3.79014e-04 4.32010e-04   1.206870   0.930709   1.050540
## 19962 1.55317e-03 1.37795e-03 1.14304e-03   0.640391   0.573849   0.685643
## 19999 8.16667e-04 1.26139e-03 1.36611e-03  -0.243400   0.635922   1.156450
## 20000 1.24650e-03 1.72167e-03 1.74439e-03  -0.207223   1.104400   1.037880
## 20002 2.43118e-03 3.10507e-03 2.86397e-03   0.924093   0.736868   0.386692
##         SNP
## 7         7
## 9         9
## 10       10
## 903     903
## 1414   1414
## 2251   2251
## 3399   3399
## 3400   3400
## 3793   3793
## 3946   3946
## 13219 13219
## 13220 13220
## 16265 16265
## 16267 16267
## 16268 16268
## 16271 16271
## 19739 19739
## 19962 19962
## 19999 19999
## 20000 20000
## 20002 20002
length(div.outliers.AUUK.lowFstUSAU$SNP)
## [1] 21
div.outliers.USUK.lowFstUSAU <- div.outliers.USUK[which(div.outliers.USUK$WEIGHTED_FST_USAU < 0.01 ),] 
div.outliers.USUK.lowFstUSAU
##       CHROM BIN_START   BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 903   12.00   3500001   3550000        313          0.184156     0.1390400
## 3399   1.25  25150001  25200000         80          0.129138     0.1210420
## 3772   1.25  43800001  43850000          9          0.233478     0.2201170
## 3773   1.25  43850001  43900000          9          0.164466     0.1563530
## 3774   1.25  43900001  43950000          8          0.222953     0.2224730
## 3781   1.25  44250001  44300000          8          0.198336     0.1773510
## 3788   1.25  44600001  44650000          8          0.183673     0.1641710
## 3790   1.25  44700001  44750000          9          0.198634     0.1724300
## 3791   1.25  44750001  44800000          4          0.126050     0.1050630
## 3792   1.25  44800001  44850000          5          0.158329     0.1520360
## 3793   1.25  44850001  44900000          7          0.150255     0.1512780
## 3826   1.25  46500001  46550000         16          0.119742     0.1104310
## 3941   1.25  52250001  52300000          6          0.144998     0.1072330
## 3950   1.25  52700001  52750000          3          0.122153     0.0888192
## 6514   1.00 106850001 106900000         40          0.132203     0.1245470
## 13217  4.50   6000001   6050000         87          0.127251     0.1166620
## 13218  4.50   6050001   6100000        122          0.124097     0.1208020
## 13219  4.50   6100001   6150000        132          0.138117     0.1304820
## 13220  4.50   6150001   6200000         84          0.138296     0.1325750
## 14057  4.00  27600001  27650000         43          0.124484     0.1000850
## 14058  4.00  27650001  27700000         15          0.124772     0.1110270
## 14243  4.00  36900001  36950000        120          0.144461     0.1311450
## 16262  6.00   5350001   5400000         23          0.152651     0.1426320
## 16265  6.00   5500001   5550000         61          0.286859     0.2827380
## 16266  6.00   5550001   5600000         76          0.236749     0.2467770
## 16267  6.00   5600001   5650000         66          0.248827     0.2538260
## 16268  6.00   5650001   5700000         90          0.262699     0.2607650
## 16269  6.00   5700001   5750000        118          0.200669     0.1998990
## 16270  6.00   5750001   5800000         84          0.189015     0.1918170
## 16271  6.00   5800001   5850000         95          0.156992     0.1609480
## 19081  0.00  18950001  19000000        222          0.189965     0.1459400
## 19082  0.00  19000001  19050000        193          0.145196     0.1232680
## 19739  0.00  51900001  51950000         60          0.187413     0.1492570
## 19800  0.00  54950001  55000000        332          0.138968     0.1067000
## 19932  0.00  61550001  61600000        113          0.165185     0.1225240
## 19999  0.00  64900001  64950000        186          0.176547     0.1102810
## 20000  0.00  64950001  65000000        240          0.263776     0.1947360
## 20001  0.00  65000001  65050000        277          0.128128     0.0954193
## 20006  0.00  65250001  65300000        194          0.175280     0.1503040
##       WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU
## 903           0.1728570     0.1321760        0.00000000    0.00000000
## 3399          0.1871650     0.1733300        0.00000000    0.00000000
## 3772          0.1356170     0.1306400        0.00000000    0.00000000
## 3773          0.0852238     0.0765575        0.00000000    0.00000000
## 3774          0.1335340     0.1317840        0.00000000    0.00000000
## 3781          0.1177940     0.1036950        0.00646088    0.00000000
## 3788          0.1177940     0.1036950        0.00209059    0.00000000
## 3790          0.1181560     0.1062690        0.00000000    0.00000000
## 3791          0.1087810     0.0889597        0.00000000    0.00000000
## 3792          0.1012870     0.1007000        0.00000000    0.00000000
## 3793          0.1925030     0.1665460        0.00808843    0.00000000
## 3826          0.1379380     0.1391420        0.00000000    0.00899771
## 3941          0.0853294     0.0588546        0.00000000    0.00000000
## 3950          0.0778325     0.0539009        0.00000000    0.00000000
## 6514          0.1120650     0.1021280        0.00000000    0.00000000
## 13217         0.1235350     0.1125110        0.00000000    0.00000000
## 13218         0.1407820     0.1299840        0.00000000    0.00000000
## 13219         0.1494690     0.1414180        0.00000000    0.00000000
## 13220         0.1648260     0.1533640        0.00000000    0.00000000
## 14057         0.0421729     0.0368661        0.00000000    0.00000000
## 14058         0.0708094     0.0544110        0.00000000    0.00000000
## 14243         0.0396591     0.0321878        0.00000000    0.00000000
## 16262         0.1195050     0.1207770        0.00000000    0.00000000
## 16265         0.1788060     0.1780510        0.00000000    0.00000000
## 16266         0.1419130     0.1501990        0.00000000    0.00000000
## 16267         0.1550950     0.1626050        0.00000000    0.00000000
## 16268         0.1722760     0.1675040        0.00000000    0.01049480
## 16269         0.1428110     0.1457580        0.00000000    0.00000000
## 16270         0.1452970     0.1502240        0.00000000    0.00000000
## 16271         0.1555700     0.1517520        0.00498528    0.00645837
## 19081         0.0750444     0.0538881        0.00000000    0.00000000
## 19082         0.0193002     0.0267927        0.00000000    0.00000000
## 19739         0.1588870     0.1264000        0.00694353    0.00453962
## 19800         0.0791149     0.0666468        0.00000000    0.00000000
## 19932         0.0833852     0.0580127        0.00000000    0.00000000
## 19999         0.2507440     0.1871510        0.00420950    0.00244991
## 20000         0.2302480     0.1626550        0.00000000    0.00000000
## 20001         0.1301150     0.1041760        0.00000000    0.00000000
## 20006         0.1216110     0.1062280        0.00000000    0.00000000
##             PI_UK       PI_US       PI_AU TajimaD_UK TajimaD_US TajimaD_AU
## 903   1.58583e-03 2.29027e-03 2.38120e-03   0.322469   1.214250  1.2308100
## 3399  6.12500e-04 8.16000e-04 7.46855e-04   1.160520   2.972800  2.4740600
## 3772  9.01667e-05 2.65000e-05 4.65000e-05   2.432020  -1.878520 -0.5244280
## 3773  8.30000e-05 4.03333e-05 4.91667e-05   1.946800  -0.941941 -0.3438820
## 3774  8.16667e-05 2.76668e-05 4.20002e-05   2.500830  -1.353500 -0.3733890
## 3781  7.41667e-05 2.76667e-05 3.45000e-05   1.940080  -1.536610 -0.6423260
## 3788  7.41667e-05 2.98333e-05 3.45000e-05   1.940080  -1.374610 -0.6423260
## 3790  8.61667e-05 2.65001e-05 3.85002e-05   2.161200  -1.833620 -0.6350750
## 3791  3.55000e-05 1.96667e-05 2.45000e-05   1.478110  -0.576488  0.0507059
## 3792  4.75000e-05 2.45000e-05 3.60003e-05   1.898740  -0.616373  0.2162880
## 3793  6.46667e-05 4.11669e-05 4.38335e-05   1.877030   0.118964  0.2394070
## 3826  1.56500e-04 1.14501e-04 1.17334e-04   2.455710   0.890794  1.0316700
## 3941  4.66669e-05 1.55001e-05 2.20002e-05   1.110000  -1.317040 -0.6062470
## 3950  2.56667e-05 1.18333e-05 1.58333e-05   1.216020  -1.001800 -0.3605060
## 6514  2.03833e-04 3.69672e-04 3.52672e-04  -0.557074   2.328280  2.7553400
## 13217 7.23833e-04 2.57341e-04 3.00170e-04   1.775250  -2.075340 -1.8571700
## 13218 1.04117e-03 4.05516e-04 3.68677e-04   2.007180  -1.910490 -2.1351400
## 13219 1.16983e-03 4.09190e-04 3.88846e-04   2.126910  -2.148800 -2.1798400
## 13220 7.55674e-04 2.66003e-04 2.38502e-04   2.154800  -2.022640 -2.3049900
## 14057 3.81000e-04 3.27002e-04 3.94502e-04   2.124500   1.261110  2.2358600
## 14058 1.20500e-04 9.08347e-05 1.06668e-04   1.303780   1.346540  1.2527000
## 14243 1.05652e-03 8.72538e-04 9.60703e-04   2.022060   1.051440  1.8017000
## 16262 5.05000e-05 1.48335e-04 1.75833e-04  -0.253609   0.363975  0.8790340
## 16265 2.88333e-05 5.33346e-04 4.75333e-04   1.723710   2.056500  1.3396700
## 16266 9.40005e-05 6.90347e-04 6.21012e-04   0.189762   2.226990  1.4170800
## 16267 5.08333e-05 5.92009e-04 5.25841e-04   0.721746   2.161640  1.5104900
## 16268 6.93333e-05 7.88034e-04 6.67837e-04  -1.293670   2.136270  1.3758000
## 16269 1.06667e-04 9.61546e-04 9.50343e-04  -1.925220   1.627650  1.3995400
## 16270 7.71667e-05 6.45525e-04 6.42512e-04  -0.682127   1.393540  1.2760900
## 16271 1.58333e-04 6.64365e-04 7.56515e-04  -0.174387   1.129640  1.4271600
## 19081 1.18901e-03 1.24461e-03 1.22436e-03   0.862748   0.758027  0.7976620
## 19082 1.11667e-03 1.30971e-03 1.30786e-03   2.029490   0.784797  1.0812900
## 19739 3.98671e-04 3.79014e-04 4.32010e-04   1.206870   0.930709  1.0505400
## 19800 2.37000e-03 1.99995e-03 2.30563e-03   1.246390   0.748836  1.2112400
## 19932 6.72170e-04 8.29064e-04 8.34358e-04   0.502783   1.071620  1.1047700
## 19999 8.16667e-04 1.26139e-03 1.36611e-03  -0.243400   0.635922  1.1564500
## 20000 1.24650e-03 1.72167e-03 1.74439e-03  -0.207223   1.104400  1.0378800
## 20001 1.53219e-03 1.96551e-03 2.01766e-03   0.304739   1.123120  1.3799400
## 20006 1.57217e-03 1.30438e-03 1.47139e-03   1.530390   1.099090  1.1772900
##         SNP
## 903     903
## 3399   3399
## 3772   3772
## 3773   3773
## 3774   3774
## 3781   3781
## 3788   3788
## 3790   3790
## 3791   3791
## 3792   3792
## 3793   3793
## 3826   3826
## 3941   3941
## 3950   3950
## 6514   6514
## 13217 13217
## 13218 13218
## 13219 13219
## 13220 13220
## 14057 14057
## 14058 14058
## 14243 14243
## 16262 16262
## 16265 16265
## 16266 16266
## 16267 16267
## 16268 16268
## 16269 16269
## 16270 16270
## 16271 16271
## 19081 19081
## 19082 19082
## 19739 19739
## 19800 19800
## 19932 19932
## 19999 19999
## 20000 20000
## 20001 20001
## 20006 20006
length(div.outliers.USUK.lowFstUSAU$SNP)
## [1] 39

Possible parallel “selection” ?

Histograms of FST, pi

What’s the statistical distribution of these values?

summary(div$WEIGHTED_FST_AUUK)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.01132 0.02659 0.03258 0.04437 0.40190
summary(div$WEIGHTED_FST_UKUS)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000000 0.0001839 0.0125022 0.0187334 0.0263484 0.3414900
summary(div$WEIGHTED_FST_USAU)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.01724 0.03414 0.04038 0.05435 0.48283
lab.AU <- rep("AU.UK",length(div$WEIGHTED_FST_AUUK))
lab.US <- rep("UK.US",length(div$WEIGHTED_FST_UKUS))
Fst.group <- c(lab.AU,lab.US)
Fst.hist.data <- c(div$WEIGHTED_FST_AUUK,div$WEIGHTED_FST_USUK)
Fst.hist <- data.frame(Fst = Fst.hist.data, population = Fst.group)
pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/HistDensity_Fst.pdf",width=4,height=3)
ggplot(Fst.hist, aes(x=Fst, y=..density.., fill=population)) +
  theme_classic() +
  geom_density(alpha=0.5,lwd=0.5) +
  scale_fill_manual(values=c("#F2C14E","#2c81a8")) + xlim(0,0.5) + 
  xlab("Fst") + labs(fill="Population") + 
  geom_vline(xintercept=0.03,colour=alpha("#F2C14E"),linetype="dashed", size=1) +
  geom_vline(xintercept=0.01,colour=alpha("#2c81a8"),linetype="dashed", size=1) +
  geom_vline(xintercept=0.08,colour=alpha("gray50"),linetype="dotted", size=0.5)
dev.off()
## quartz_off_screen 
##                 2
ggplot(Fst.hist, aes(x=Fst, y=..density.., fill=population)) +
  theme_classic() +
  geom_density(alpha=0.5,lwd=0.5) +
  scale_fill_manual(values=c("#F2C14E","#2c81a8")) + xlim(0,0.5) + 
  xlab("Fst") + labs(fill="Population") + 
  geom_vline(xintercept=0.03,colour=alpha("#F2C14E"),linetype="dashed", size=1) +
  geom_vline(xintercept=0.01,colour=alpha("#2c81a8"),linetype="dashed", size=1) +
  geom_vline(xintercept=0.08,colour=alpha("gray50"),linetype="dotted", size=0.5)

summary(div$PI_AU)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000025 0.0027682 0.0040090 0.0038645 0.0050565 0.0138998
summary(div$PI_UK)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000025 0.0028343 0.0041312 0.0039757 0.0051943 0.0141869
summary(div$PI_US)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 4.667e-06 2.753e-03 4.016e-03 3.863e-03 5.045e-03 1.409e-02
lab.AU <- rep("AU",length(div$PI_AU))
lab.US <- rep("US",length(div$PI_US))
lab.UK <- rep("UK",length(div$PI_UK))
group <- c(lab.AU,lab.US,lab.UK)
pi.hist.data <- c(div$PI_UK,div$PI_US,div$PI_AU)
pi.hist.lab <- data.frame(pi = pi.hist.data, population = group)
str(pi.hist.lab)
## 'data.frame':    60213 obs. of  2 variables:
##  $ pi        : num  0.001053 0.002189 0.002212 0.000915 0.000879 ...
##  $ population: Factor w/ 3 levels "AU","UK","US": 1 1 1 1 1 1 1 1 1 1 ...
ggplot(pi.hist.lab, aes(x=pi, y=..density.., fill=population)) +
  geom_density(alpha=0.8,lwd=0.5) + theme_classic() +
  scale_fill_manual(values=c("black","#2c81a8","#F2C14E")) + xlim(-0.0001,0.02) + 
  xlab("Pi") + labs(fill="Population") +
  geom_vline(xintercept=mean(div$PI_AU),colour=alpha("#F2C14E"),linetype="dashed", size=1) +
  geom_vline(xintercept=mean(div$PI_US),colour=alpha("#2c81a8"),linetype="dashed", size=1) +
  theme(legend.position="none")

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/HistDensity_Pi.pdf",width=4,height=3)
ggplot(pi.hist.lab, aes(x=pi, y=..density.., fill=population)) +
  geom_density(alpha=0.8,lwd=0.5) + theme_classic() +
  scale_fill_manual(values=c("black","#2c81a8","#F2C14E")) + xlim(-0.0001,0.02) + 
  xlab("Pi") + labs(fill="Population") +
  geom_vline(xintercept=mean(div$PI_US),colour=alpha("#2c81a8"),linetype="dashed", size=1) +
  geom_vline(xintercept=mean(div$PI_AU),colour=alpha("#F2C14E"),linetype="dashed", size=1) +
  theme(legend.position="none")
dev.off()
## quartz_off_screen 
##                 2

Average nucleotide diversity for both invasions is the same (0.003). There are two vertical lines overlaid in the plot above.

ggplot(data=div) +
  geom_point(aes(x=PI_UK, y=PI_US),col="#2c81a8",cex=0.7) +
  xlab("") + ylab("") + xlim(0,0.02) + ylim(0,0.02) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_US),span=0.2,method="loess",col="black",lwd=0.5)
## `geom_smooth()` using formula 'y ~ x'

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Pi_USvsUK.pdf",width=2,height=2)
ggplot(data=div) +
  geom_point(aes(x=PI_UK, y=PI_US),col="#2c81a8",cex=0.7) +
  xlab("") + ylab("") + xlim(0,0.02) + ylim(0,0.02) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_US),span=0.2,method="loess",col="black",lwd=0.5)
## `geom_smooth()` using formula 'y ~ x'
dev.off()
## quartz_off_screen 
##                 2
ggplot(data=div.outliers.USUK) +
  geom_point(aes(x=PI_UK, y=PI_US),col="#2c81a8",cex=0.7) +
  xlab("") + ylab("") + xlim(0,0.01) + ylim(0,0.01) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_US),span=0.2,method="loess",col="black",lwd=0.5)
## `geom_smooth()` using formula 'y ~ x'

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/PiOutliers_USvsUK.pdf",width=2,height=2)
ggplot(data=div.outliers.USUK) +
  geom_point(aes(x=PI_UK, y=PI_US),col="#2c81a8",cex=0.7) +
  xlab("") + ylab("") + xlim(0,0.01) + ylim(0,0.01) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_US),span=0.2,method="loess",col="black",lwd=0.5)
## `geom_smooth()` using formula 'y ~ x'
dev.off()
## quartz_off_screen 
##                 2
ggplot(data=div) +
  geom_point(aes(x=PI_UK, y=PI_AU),col="#F2C14E",cex=0.7) +
  xlab("") + ylab("") +
  xlim(0,0.02) + ylim(0,0.02) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_AU),span=0.2,method="loess",col="black",lwd=0.5) 
## `geom_smooth()` using formula 'y ~ x'

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Pi_AUvsUK.pdf",width=2,height=2)
ggplot(data=div) +
  geom_point(aes(x=PI_UK, y=PI_AU),col="#F2C14E",cex=0.7) +
  xlab("") + ylab("") +
  xlim(0,0.02) + ylim(0,0.02) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_AU),span=0.2,method="loess",col="black",lwd=0.5) 
## `geom_smooth()` using formula 'y ~ x'
dev.off()
## quartz_off_screen 
##                 2
ggplot(data=div.outliers.AUUK) +
  geom_point(aes(x=PI_UK, y=PI_AU),col="#F2C14E",cex=0.7) +
  xlab("") + ylab("") +
  xlim(0,0.01) + ylim(0,0.01) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_AU),span=0.2,method="loess",col="black",lwd=0.5) 
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing missing values (geom_smooth).

dev.off()
## null device 
##           1
pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Pi_AUvsUK.pdf",width=2,height=2)
ggplot(data=div.outliers.AUUK) +
  geom_point(aes(x=PI_UK, y=PI_AU),col="#F2C14E",cex=0.7) +
  xlab("") + ylab("") +
  xlim(0,0.01) + ylim(0,0.01) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_AU),span=0.2,method="loess",col="black",lwd=0.5) 
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing missing values (geom_smooth).
dev.off()
## null device 
##           1

Step 4: What’s the impact of the genetic bottlenecks and subsequent expansion on genetic diversity in each invasion?

What’s the difference in diversity between native and invasive ranges?

div$piUK.piAU <- div$PI_UK - div$PI_AU
div$piUK.piUS <- div$PI_UK - div$PI_US
summary(div$piUK.piAU)
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -1.552e-03 -1.935e-05  9.278e-05  1.112e-04  2.319e-04  2.028e-03
qqnorm(div$piUK.piAU, pch = 16)
qqline(div$piUK.piAU, pch = 16)

summary(div$piUK.piUS)
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -1.381e-03 -2.535e-06  1.035e-04  1.132e-04  2.277e-04  1.762e-03
qqnorm(div$piUK.piUS, pch = 16)
qqline(div$piUK.piUS, pch = 16)

div.hiUKpi.vsAU <- div[which(div$piUK.piAU > 0),]
div.hiUKpi.vsUS <- div[which(div$piUK.piUS > 0),]
div.hiUKpi.both <- div.hiUKpi.vsAU[which(div$piUK.piUS > 0),]

length(div.hiUKpi.vsAU$piUK.piAU)/length(div$piUK.piAU) # % of windows that have higher pi in native
## [1] 0.7049973
length(div.hiUKpi.vsUS$piUK.piUS)/length(div$piUK.piUS) 
## [1] 0.7432116
length(div.hiUKpi.both$piUK.piUS)/length(div$piUK.piUS) # % of windows with higher pi in both invasive ranges
## [1] 0.7432116
div.hifst.AUUK$piUK.piAU <- div.hifst.AUUK$PI_UK - div.hifst.AUUK$PI_AU
div.hifst.UKUS$piUK.piUS <- div.hifst.UKUS$PI_UK - div.hifst.UKUS$PI_US
summary(div.hifst.AUUK$piUK.piAU)
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -1.197e-03 -4.330e-05  3.283e-05  1.246e-04  2.897e-04  2.028e-03
summary(div.hifst.UKUS$piUK.piUS)
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -1.198e-03 -2.150e-05  2.550e-05  6.589e-05  1.648e-04  1.762e-03

When comparing ancestral diversity to a bottlenecked invasive population, we’d expect to see lower diversity in the invasive range overall. This difference would be more pronounced in regions that had differentiated from the native range (e.g., where drift and/or selection are more pronounced).

Here, difference in pi > 0 means that diversity is higher in the native range than in the invasive.

lab.AU <- rep("AU",length(div$piUK.piAU))
lab.US <- rep("US",length(div$piUK.piUS))
group <- c(lab.AU,lab.US)
pi.hist.data <- c(div$piUK.piUS,div$piUK.piAU)
pi.hist.lab <- data.frame(pi = pi.hist.data, population = group)
str(pi.hist.lab)
## 'data.frame':    40142 obs. of  2 variables:
##  $ pi        : num  -1.04e-05 -1.83e-04 2.09e-04 8.63e-05 1.79e-04 ...
##  $ population: Factor w/ 2 levels "AU","US": 1 1 1 1 1 1 1 1 1 1 ...
ggplot(pi.hist.lab, aes(x=pi, y=..density.., fill=population)) +
  geom_density(alpha=0.8,lwd=0.5) + theme_classic() +
  scale_fill_manual(values=c("#2c81a8","#F2C14E")) + 
  xlab("DIfference in pi") + labs(fill="Population") +
  geom_vline(xintercept=mean(div$piUK.piAU),colour=alpha("#F2C14E"), size=1) +
  geom_vline(xintercept=mean(div$piUK.piUS),colour=alpha("#2c81a8"), size=1) +
  geom_vline(xintercept=0,colour="black",size=0.5) +
  theme(legend.position="none")

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/HistDensity_PiDifference.pdf",width=4,height=3)
ggplot(pi.hist.lab, aes(x=pi, y=..density.., fill=population)) +
  geom_density(alpha=0.8,lwd=0.5) + theme_classic() +
  scale_fill_manual(values=c("#2c81a8","#F2C14E")) + 
  xlab("Difference in pi") + labs(fill="Population") +
  geom_vline(xintercept=mean(div$piUK.piAU),colour=alpha("#F2C14E"), size=1) +
  geom_vline(xintercept=mean(div$piUK.piUS),colour=alpha("#2c81a8"), size=1) +
  geom_vline(xintercept=0,colour="black",size=0.5) +
  theme(legend.position="none")
dev.off()
## quartz_off_screen 
##                 2

What about with high-fst (FST > 0.1) windows only?

lab.AU <- rep("AU",length(div.hifst.AUUK$piUK.piAU))
lab.US <- rep("US",length(div.hifst.UKUS$piUK.piUS))
group <- c(lab.AU,lab.US)
pi.hist.data <- c(div.hifst.UKUS$piUK.piUS,div.hifst.AUUK$piUK.piAU)
pi.hist.lab <- data.frame(pi = pi.hist.data, population = group)
str(pi.hist.lab)
## 'data.frame':    967 obs. of  2 variables:
##  $ pi        : num  1.88e-04 8.97e-05 1.13e-04 4.36e-04 2.19e-04 ...
##  $ population: Factor w/ 2 levels "AU","US": 1 1 1 1 1 1 1 1 1 1 ...
ggplot(pi.hist.lab, aes(x=pi, y=..density.., fill=population)) +
  geom_density(alpha=0.8,lwd=0.5) + theme_classic() +
  scale_fill_manual(values=c("#2c81a8","#F2C14E")) + 
  xlab("Difference in pi") + labs(fill="Population") +
  geom_vline(xintercept=mean(div.hifst.AUUK$piUK.piAU),colour=alpha("#F2C14E"), size=1) +
  geom_vline(xintercept=mean(div.hifst.UKUS$piUK.piUS),colour=alpha("#2c81a8"), size=1) +
  geom_vline(xintercept=0,colour="black",size=0.5) +
  theme(legend.position="none")

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/HistDensity_PiDifference_HiFst.pdf",width=4,height=3)
ggplot(pi.hist.lab, aes(x=pi, y=..density.., fill=population)) +
  geom_density(alpha=0.8,lwd=0.5) + theme_classic() +
  scale_fill_manual(values=c("#2c81a8","#F2C14E")) + 
  xlab("DIfference in pi") + labs(fill="Population") +
  geom_vline(xintercept=mean(div.hifst.AUUK$piUK.piAU),colour=alpha("#F2C14E"), size=1) +
  geom_vline(xintercept=mean(div.hifst.UKUS$piUK.piUS),colour=alpha("#2c81a8"), size=1) +
  geom_vline(xintercept=0,colour="black",size=0.5) +
  theme(legend.position="none")
dev.off()
## quartz_off_screen 
##                 2

Novel diversity in invasions

In a given window, if diversity in the invasive range is higher than that of the native range, it is possible that those variants are novel mutations. This filtering will tell us whether we should look at particular genotypes in these regions.

Possibly “novel” diversity (e.g., lower than average native diversity and higher than average invasive)

div.lownatpi <- div[which(div$PI_UK < mean(div$PI_UK)),]
length(div.lownatpi$SNP)
## [1] 9337
unique(div.lownatpi$CHROM)
##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00  1.25  1.75
## [13]  1.00 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00
## [25]  4.50  4.00  5.00  6.00  7.00  8.00  9.00 29.00  0.00
length(unique(div.lownatpi$CHROM))
## [1] 33

Number of SNPs have below-average diversity in the native range. Of these SNPs, how many have higher-than average pi in the invasive range (AU or US)?

summary(div.lownatpi$PI_AU)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000025 0.0015566 0.0026495 0.0023695 0.0033432 0.0052840
div.novelAUpi <- div.lownatpi[which(div.lownatpi$PI_AU > mean(div.lownatpi$PI_AU)),]
unique(div.novelAUpi$CHROM) # which chromosomes 
##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00  4.50
## [25]  4.00  5.00  6.00  7.00  8.00  9.00 29.00  0.00
length(div.novelAUpi$CHROM) # number of windows
## [1] 5362
length(div.novelAUpi$SNP)/length(div.lownatpi$SNP)
## [1] 0.5742744

% of low native diversity SNPs are higher in AU diversity.

summary(div.lownatpi$PI_US)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 4.667e-06 1.541e-03 2.632e-03 2.357e-03 3.334e-03 4.800e-03
div.novelUSpi <- div.lownatpi[which(div.lownatpi$PI_US > mean(div.lownatpi$PI_US)),]
length(div.novelUSpi$SNP)/length(div.lownatpi$SNP)
## [1] 0.5774874

% of low native diversity SNPs are higher in US diversity.

unique(div.novelUSpi$CHROM)
##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00  4.50
## [25]  4.00  5.00  6.00  7.00  8.00  9.00 29.00  0.00
length(div.novelUSpi$CHROM)
## [1] 5392
intersect(div.novelAUpi$CHROM,div.novelUSpi$CHROM)
##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00  4.50
## [25]  4.00  5.00  6.00  7.00  8.00  9.00 29.00  0.00
length(intersect(div.novelAUpi$CHROM,div.novelUSpi$CHROM))
## [1] 32
#intersect(div.novelAUpi$SNP,div.novelUSpi$SNP)
length(intersect(div.novelAUpi$SNP,div.novelUSpi$SNP))
## [1] 5177

What’s going on at outlier windows in particular?

div.outliers.hiAUpi <- div.outliers.AUUK[which(div.outliers.AUUK$PI_AU > div.outliers.AUUK$PI_UK),]
unique(div.outliers.hiAUpi$CHROM)
##  [1] 12.00 13.00  1.25  1.00 23.00 27.00  2.00  3.00  4.50  5.00  6.00  0.00
length(div.outliers.hiAUpi$SNP)
## [1] 91
length(div.outliers.AUUK$SNP)
## [1] 201
length(div.outliers.hiAUpi$SNP)/length(div.outliers.AUUK$SNP)
## [1] 0.4527363

85% of FST outlier windows have higher diversity in AU

div.outliers.hiUSpi <- div.outliers.USUK[which(div.outliers.USUK$PI_US > div.outliers.USUK$PI_UK),]
unique(div.outliers.hiUSpi$CHROM) 
## [1] 12.00 17.00  1.25  1.00  2.00  3.00  4.00  6.00  0.00
length(div.outliers.hiUSpi$SNP) 
## [1] 61
length(div.outliers.USUK$SNP)
## [1] 201
length(div.outliers.hiUSpi$SNP)/length(div.outliers.USUK$SNP)
## [1] 0.3034826

Only 38% of FST outlier windows have higher diversity in US

intersect(div.outliers.hiUSpi$CHROM,div.outliers.hiAUpi$CHROM)
## [1] 12.00  1.25  1.00  2.00  3.00  6.00  0.00

None of these regions overlap between invasions.

Figures: Diversity underlying candidate peaks

These plots are based on code from Gemma Clucas.

Chromosome 2

chrom2.div <- div[which(div$CHROM==2),]
unique(chrom2.div$SNP)
##    [1]  7875  7876  7877  7878  7879  7880  7881  7882  7883  7884  7885  7886
##   [13]  7887  7888  7889  7890  7891  7892  7893  7894  7895  7896  7897  7898
##   [25]  7899  7900  7901  7902  7903  7904  7905  7906  7907  7908  7909  7910
##   [37]  7911  7912  7913  7914  7915  7916  7917  7918  7919  7920  7921  7922
##   [49]  7923  7924  7925  7926  7927  7928  7929  7930  7931  7932  7933  7934
##   [61]  7935  7936  7937  7938  7939  7940  7941  7942  7943  7944  7945  7946
##   [73]  7947  7948  7949  7950  7951  7952  7953  7954  7955  7956  7957  7958
##   [85]  7959  7960  7961  7962  7963  7964  7965  7966  7967  7968  7969  7970
##   [97]  7971  7972  7973  7974  7975  7976  7977  7978  7979  7980  7981  7982
##  [109]  7983  7984  7985  7986  7987  7988  7989  7990  7991  7992  7993  7994
##  [121]  7995  7996  7997  7998  7999  8000  8001  8002  8003  8004  8005  8006
##  [133]  8007  8008  8009  8010  8011  8012  8013  8014  8015  8016  8017  8018
##  [145]  8019  8020  8021  8022  8023  8024  8025  8026  8027  8028  8029  8030
##  [157]  8031  8032  8033  8034  8035  8036  8037  8038  8039  8040  8041  8042
##  [169]  8043  8044  8045  8046  8047  8048  8049  8050  8051  8052  8053  8054
##  [181]  8055  8056  8057  8058  8059  8060  8061  8062  8063  8064  8065  8066
##  [193]  8067  8068  8069  8070  8071  8072  8073  8074  8075  8076  8077  8078
##  [205]  8079  8080  8081  8082  8083  8084  8085  8086  8087  8088  8089  8090
##  [217]  8091  8092  8093  8094  8095  8096  8097  8098  8099  8100  8101  8102
##  [229]  8103  8104  8105  8106  8107  8108  8109  8110  8111  8112  8113  8114
##  [241]  8115  8116  8117  8118  8119  8120  8121  8122  8123  8124  8125  8126
##  [253]  8127  8128  8129  8130  8131  8132  8133  8134  8135  8136  8137  8138
##  [265]  8139  8140  8141  8142  8143  8144  8145  8146  8147  8148  8149  8150
##  [277]  8151  8152  8153  8154  8155  8156  8157  8158  8159  8160  8161  8162
##  [289]  8163  8164  8165  8166  8167  8168  8169  8170  8171  8172  8173  8174
##  [301]  8175  8176  8177  8178  8179  8180  8181  8182  8183  8184  8185  8186
##  [313]  8187  8188  8189  8190  8191  8192  8193  8194  8195  8196  8197  8198
##  [325]  8199  8200  8201  8202  8203  8204  8205  8206  8207  8208  8209  8210
##  [337]  8211  8212  8213  8214  8215  8216  8217  8218  8219  8220  8221  8222
##  [349]  8223  8224  8225  8226  8227  8228  8229  8230  8231  8232  8233  8234
##  [361]  8235  8236  8237  8238  8239  8240  8241  8242  8243  8244  8245  8246
##  [373]  8247  8248  8249  8250  8251  8252  8253  8254  8255  8256  8257  8258
##  [385]  8259  8260  8261  8262  8263  8264  8265  8266  8267  8268  8269  8270
##  [397]  8271  8272  8273  8274  8275  8276  8277  8278  8279  8280  8281  8282
##  [409]  8283  8284  8285  8286  8287  8288  8289  8290  8291  8292  8293  8294
##  [421]  8295  8296  8297  8298  8299  8300  8301  8302  8303  8304  8305  8306
##  [433]  8307  8308  8309  8310  8311  8312  8313  8314  8315  8316  8317  8318
##  [445]  8319  8320  8321  8322  8323  8324  8325  8326  8327  8328  8329  8330
##  [457]  8331  8332  8333  8334  8335  8336  8337  8338  8339  8340  8341  8342
##  [469]  8343  8344  8345  8346  8347  8348  8349  8350  8351  8352  8353  8354
##  [481]  8355  8356  8357  8358  8359  8360  8361  8362  8363  8364  8365  8366
##  [493]  8367  8368  8369  8370  8371  8372  8373  8374  8375  8376  8377  8378
##  [505]  8379  8380  8381  8382  8383  8384  8385  8386  8387  8388  8389  8390
##  [517]  8391  8392  8393  8394  8395  8396  8397  8398  8399  8400  8401  8402
##  [529]  8403  8404  8405  8406  8407  8408  8409  8410  8411  8412  8413  8414
##  [541]  8415  8416  8417  8418  8419  8420  8421  8422  8423  8424  8425  8426
##  [553]  8427  8428  8429  8430  8431  8432  8433  8434  8435  8436  8437  8438
##  [565]  8439  8440  8441  8442  8443  8444  8445  8446  8447  8448  8449  8450
##  [577]  8451  8452  8453  8454  8455  8456  8457  8458  8459  8460  8461  8462
##  [589]  8463  8464  8465  8466  8467  8468  8469  8470  8471  8472  8473  8474
##  [601]  8475  8476  8477  8478  8479  8480  8481  8482  8483  8484  8485  8486
##  [613]  8487  8488  8489  8490  8491  8492  8493  8494  8495  8496  8497  8498
##  [625]  8499  8500  8501  8502  8503  8504  8505  8506  8507  8508  8509  8510
##  [637]  8511  8512  8513  8514  8515  8516  8517  8518  8519  8520  8521  8522
##  [649]  8523  8524  8525  8526  8527  8528  8529  8530  8531  8532  8533  8534
##  [661]  8535  8536  8537  8538  8539  8540  8541  8542  8543  8544  8545  8546
##  [673]  8547  8548  8549  8550  8551  8552  8553  8554  8555  8556  8557  8558
##  [685]  8559  8560  8561  8562  8563  8564  8565  8566  8567  8568  8569  8570
##  [697]  8571  8572  8573  8574  8575  8576  8577  8578  8579  8580  8581  8582
##  [709]  8583  8584  8585  8586  8587  8588  8589  8590  8591  8592  8593  8594
##  [721]  8595  8596  8597  8598  8599  8600  8601  8602  8603  8604  8605  8606
##  [733]  8607  8608  8609  8610  8611  8612  8613  8614  8615  8616  8617  8618
##  [745]  8619  8620  8621  8622  8623  8624  8625  8626  8627  8628  8629  8630
##  [757]  8631  8632  8633  8634  8635  8636  8637  8638  8639  8640  8641  8642
##  [769]  8643  8644  8645  8646  8647  8648  8649  8650  8651  8652  8653  8654
##  [781]  8655  8656  8657  8658  8659  8660  8661  8662  8663  8664  8665  8666
##  [793]  8667  8668  8669  8670  8671  8672  8673  8674  8675  8676  8677  8678
##  [805]  8679  8680  8681  8682  8683  8684  8685  8686  8687  8688  8689  8690
##  [817]  8691  8692  8693  8694  8695  8696  8697  8698  8699  8700  8701  8702
##  [829]  8703  8704  8705  8706  8707  8708  8709  8710  8711  8712  8713  8714
##  [841]  8715  8716  8717  8718  8719  8720  8721  8722  8723  8724  8725  8726
##  [853]  8727  8728  8729  8730  8731  8732  8733  8734  8735  8736  8737  8738
##  [865]  8739  8740  8741  8742  8743  8744  8745  8746  8747  8748  8749  8750
##  [877]  8751  8752  8753  8754  8755  8756  8757  8758  8759  8760  8761  8762
##  [889]  8763  8764  8765  8766  8767  8768  8769  8770  8771  8772  8773  8774
##  [901]  8775  8776  8777  8778  8779  8780  8781  8782  8783  8784  8785  8786
##  [913]  8787  8788  8789  8790  8791  8792  8793  8794  8795  8796  8797  8798
##  [925]  8799  8800  8801  8802  8803  8804  8805  8806  8807  8808  8809  8810
##  [937]  8811  8812  8813  8814  8815  8816  8817  8818  8819  8820  8821  8822
##  [949]  8823  8824  8825  8826  8827  8828  8829  8830  8831  8832  8833  8834
##  [961]  8835  8836  8837  8838  8839  8840  8841  8842  8843  8844  8845  8846
##  [973]  8847  8848  8849  8850  8851  8852  8853  8854  8855  8856  8857  8858
##  [985]  8859  8860  8861  8862  8863  8864  8865  8866  8867  8868  8869  8870
##  [997]  8871  8872  8873  8874  8875  8876  8877  8878  8879  8880  8881  8882
## [1009]  8883  8884  8885  8886  8887  8888  8889  8890  8891  8892  8893  8894
## [1021]  8895  8896  8897  8898  8899  8900  8901  8902  8903  8904  8905  8906
## [1033]  8907  8908  8909  8910  8911  8912  8913  8914  8915  8916  8917  8918
## [1045]  8919  8920  8921  8922  8923  8924  8925  8926  8927  8928  8929  8930
## [1057]  8931  8932  8933  8934  8935  8936  8937  8938  8939  8940  8941  8942
## [1069]  8943  8944  8945  8946  8947  8948  8949  8950  8951  8952  8953  8954
## [1081]  8955  8956  8957  8958  8959  8960  8961  8962  8963  8964  8965  8966
## [1093]  8967  8968  8969  8970  8971  8972  8973  8974  8975  8976  8977  8978
## [1105]  8979  8980  8981  8982  8983  8984  8985  8986  8987  8988  8989  8990
## [1117]  8991  8992  8993  8994  8995  8996  8997  8998  8999  9000  9001  9002
## [1129]  9003  9004  9005  9006  9007  9008  9009  9010  9011  9012  9013  9014
## [1141]  9015  9016  9017  9018  9019  9020  9021  9022  9023  9024  9025  9026
## [1153]  9027  9028  9029  9030  9031  9032  9033  9034  9035  9036  9037  9038
## [1165]  9039  9040  9041  9042  9043  9044  9045  9046  9047  9048  9049  9050
## [1177]  9051  9052  9053  9054  9055  9056  9057  9058  9059  9060  9061  9062
## [1189]  9063  9064  9065  9066  9067  9068  9069  9070  9071  9072  9073  9074
## [1201]  9075  9076  9077  9078  9079  9080  9081  9082  9083  9084  9085  9086
## [1213]  9087  9088  9089  9090  9091  9092  9093  9094  9095  9096  9097  9098
## [1225]  9099  9100  9101  9102  9103  9104  9105  9106  9107  9108  9109  9110
## [1237]  9111  9112  9113  9114  9115  9116  9117  9118  9119  9120  9121  9122
## [1249]  9123  9124  9125  9126  9127  9128  9129  9130  9131  9132  9133  9134
## [1261]  9135  9136  9137  9138  9139  9140  9141  9142  9143  9144  9145  9146
## [1273]  9147  9148  9149  9150  9151  9152  9153  9154  9155  9156  9157  9158
## [1285]  9159  9160  9161  9162  9163  9164  9165  9166  9167  9168  9169  9170
## [1297]  9171  9172  9173  9174  9175  9176  9177  9178  9179  9180  9181  9182
## [1309]  9183  9184  9185  9186  9187  9188  9189  9190  9191  9192  9193  9194
## [1321]  9195  9196  9197  9198  9199  9200  9201  9202  9203  9204  9205  9206
## [1333]  9207  9208  9209  9210  9211  9212  9213  9214  9215  9216  9217  9218
## [1345]  9219  9220  9221  9222  9223  9224  9225  9226  9227  9228  9229  9230
## [1357]  9231  9232  9233  9234  9235  9236  9237  9238  9239  9240  9241  9242
## [1369]  9243  9244  9245  9246  9247  9248  9249  9250  9251  9252  9253  9254
## [1381]  9255  9256  9257  9258  9259  9260  9261  9262  9263  9264  9265  9266
## [1393]  9267  9268  9269  9270  9271  9272  9273  9274  9275  9276  9277  9278
## [1405]  9279  9280  9281  9282  9283  9284  9285  9286  9287  9288  9289  9290
## [1417]  9291  9292  9293  9294  9295  9296  9297  9298  9299  9300  9301  9302
## [1429]  9303  9304  9305  9306  9307  9308  9309  9310  9311  9312  9313  9314
## [1441]  9315  9316  9317  9318  9319  9320  9321  9322  9323  9324  9325  9326
## [1453]  9327  9328  9329  9330  9331  9332  9333  9334  9335  9336  9337  9338
## [1465]  9339  9340  9341  9342  9343  9344  9345  9346  9347  9348  9349  9350
## [1477]  9351  9352  9353  9354  9355  9356  9357  9358  9359  9360  9361  9362
## [1489]  9363  9364  9365  9366  9367  9368  9369  9370  9371  9372  9373  9374
## [1501]  9375  9376  9377  9378  9379  9380  9381  9382  9383  9384  9385  9386
## [1513]  9387  9388  9389  9390  9391  9392  9393  9394  9395  9396  9397  9398
## [1525]  9399  9400  9401  9402  9403  9404  9405  9406  9407  9408  9409  9410
## [1537]  9411  9412  9413  9414  9415  9416  9417  9418  9419  9420  9421  9422
## [1549]  9423  9424  9425  9426  9427  9428  9429  9430  9431  9432  9433  9434
## [1561]  9435  9436  9437  9438  9439  9440  9441  9442  9443  9444  9445  9446
## [1573]  9447  9448  9449  9450  9451  9452  9453  9454  9455  9456  9457  9458
## [1585]  9459  9460  9461  9462  9463  9464  9465  9466  9467  9468  9469  9470
## [1597]  9471  9472  9473  9474  9475  9476  9477  9478  9479  9480  9481  9482
## [1609]  9483  9484  9485  9486  9487  9488  9489  9490  9491  9492  9493  9494
## [1621]  9495  9496  9497  9498  9499  9500  9501  9502  9503  9504  9505  9506
## [1633]  9507  9508  9509  9510  9511  9512  9513  9514  9515  9516  9517  9518
## [1645]  9519  9520  9521  9522  9523  9524  9525  9526  9527  9528  9529  9530
## [1657]  9531  9532  9533  9534  9535  9536  9537  9538  9539  9540  9541  9542
## [1669]  9543  9544  9545  9546  9547  9548  9549  9550  9551  9552  9553  9554
## [1681]  9555  9556  9557  9558  9559  9560  9561  9562  9563  9564  9565  9566
## [1693]  9567  9568  9569  9570  9571  9572  9573  9574  9575  9576  9577  9578
## [1705]  9579  9580  9581  9582  9583  9584  9585  9586  9587  9588  9589  9590
## [1717]  9591  9592  9593  9594  9595  9596  9597  9598  9599  9600  9601  9602
## [1729]  9603  9604  9605  9606  9607  9608  9609  9610  9611  9612  9613  9614
## [1741]  9615  9616  9617  9618  9619  9620  9621  9622  9623  9624  9625  9626
## [1753]  9627  9628  9629  9630  9631  9632  9633  9634  9635  9636  9637  9638
## [1765]  9639  9640  9641  9642  9643  9644  9645  9646  9647  9648  9649  9650
## [1777]  9651  9652  9653  9654  9655  9656  9657  9658  9659  9660  9661  9662
## [1789]  9663  9664  9665  9666  9667  9668  9669  9670  9671  9672  9673  9674
## [1801]  9675  9676  9677  9678  9679  9680  9681  9682  9683  9684  9685  9686
## [1813]  9687  9688  9689  9690  9691  9692  9693  9694  9695  9696  9697  9698
## [1825]  9699  9700  9701  9702  9703  9704  9705  9706  9707  9708  9709  9710
## [1837]  9711  9712  9713  9714  9715  9716  9717  9718  9719  9720  9721  9722
## [1849]  9723  9724  9725  9726  9727  9728  9729  9730  9731  9732  9733  9734
## [1861]  9735  9736  9737  9738  9739  9740  9741  9742  9743  9744  9745  9746
## [1873]  9747  9748  9749  9750  9751  9752  9753  9754  9755  9756  9757  9758
## [1885]  9759  9760  9761  9762  9763  9764  9765  9766  9767  9768  9769  9770
## [1897]  9771  9772  9773  9774  9775  9776  9777  9778  9779  9780  9781  9782
## [1909]  9783  9784  9785  9786  9787  9788  9789  9790  9791  9792  9793  9794
## [1921]  9795  9796  9797  9798  9799  9800  9801  9802  9803  9804  9805  9806
## [1933]  9807  9808  9809  9810  9811  9812  9813  9814  9815  9816  9817  9818
## [1945]  9819  9820  9821  9822  9823  9824  9825  9826  9827  9828  9829  9830
## [1957]  9831  9832  9833  9834  9835  9836  9837  9838  9839  9840  9841  9842
## [1969]  9843  9844  9845  9846  9847  9848  9849  9850  9851  9852  9853  9854
## [1981]  9855  9856  9857  9858  9859  9860  9861  9862  9863  9864  9865  9866
## [1993]  9867  9868  9869  9870  9871  9872  9873  9874  9875  9876  9877  9878
## [2005]  9879  9880  9881  9882  9883  9884  9885  9886  9887  9888  9889  9890
## [2017]  9891  9892  9893  9894  9895  9896  9897  9898  9899  9900  9901  9902
## [2029]  9903  9904  9905  9906  9907  9908  9909  9910  9911  9912  9913  9914
## [2041]  9915  9916  9917  9918  9919  9920  9921  9922  9923  9924  9925  9926
## [2053]  9927  9928  9929  9930  9931  9932  9933  9934  9935  9936  9937  9938
## [2065]  9939  9940  9941  9942  9943  9944  9945  9946  9947  9948  9949  9950
## [2077]  9951  9952  9953  9954  9955  9956  9957  9958  9959  9960  9961  9962
## [2089]  9963  9964  9965  9966  9967  9968  9969  9970  9971  9972  9973  9974
## [2101]  9975  9976  9977  9978  9979  9980  9981  9982  9983  9984  9985  9986
## [2113]  9987  9988  9989  9990  9991  9992  9993  9994  9995  9996  9997  9998
## [2125]  9999 10000 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010
## [2137] 10011 10012 10013 10014 10015 10016 10017 10018 10019 10020 10021 10022
## [2149] 10023 10024 10025 10026 10027 10028 10029 10030 10031 10032 10033 10034
## [2161] 10035 10036 10037 10038 10039 10040 10041 10042 10043 10044 10045 10046
## [2173] 10047 10048 10049 10050 10051 10052 10053 10054 10055 10056 10057 10058
## [2185] 10059 10060 10061 10062 10063 10064 10065 10066 10067 10068 10069 10070
## [2197] 10071 10072 10073 10074 10075 10076 10077 10078 10079 10080 10081 10082
## [2209] 10083 10084 10085 10086 10087 10088 10089 10090 10091 10092 10093 10094
## [2221] 10095 10096 10097 10098 10099 10100 10101 10102 10103 10104 10105 10106
## [2233] 10107 10108 10109 10110 10111 10112 10113 10114 10115 10116 10117 10118
## [2245] 10119 10120 10121 10122 10123 10124 10125 10126 10127 10128 10129 10130
## [2257] 10131 10132 10133 10134 10135 10136 10137 10138 10139 10140 10141 10142
## [2269] 10143 10144 10145 10146 10147 10148 10149 10150 10151 10152 10153 10154
## [2281] 10155 10156 10157 10158 10159 10160 10161 10162 10163 10164 10165 10166
## [2293] 10167 10168 10169 10170 10171 10172 10173 10174 10175 10176 10177 10178
## [2305] 10179 10180 10181 10182 10183 10184 10185 10186 10187 10188 10189 10190
## [2317] 10191 10192 10193 10194 10195 10196 10197 10198 10199 10200 10201 10202
## [2329] 10203 10204 10205 10206 10207 10208 10209 10210 10211 10212 10213 10214
## [2341] 10215 10216 10217 10218 10219 10220 10221 10222 10223 10224 10225 10226
## [2353] 10227 10228 10229 10230 10231 10232 10233 10234 10235 10236 10237 10238
## [2365] 10239 10240 10241 10242 10243 10244 10245 10246 10247 10248 10249 10250
## [2377] 10251 10252 10253 10254 10255 10256 10257 10258 10259 10260 10261 10262
## [2389] 10263 10264 10265 10266 10267 10268 10269 10270 10271 10272 10273 10274
## [2401] 10275 10276 10277 10278 10279 10280 10281 10282 10283 10284 10285 10286
## [2413] 10287 10288 10289 10290 10291 10292 10293 10294 10295 10296 10297 10298
## [2425] 10299 10300 10301 10302 10303 10304 10305 10306 10307 10308 10309 10310
## [2437] 10311 10312 10313 10314 10315 10316 10317 10318 10319 10320 10321 10322
## [2449] 10323 10324 10325 10326 10327 10328 10329 10330 10331 10332 10333 10334
## [2461] 10335 10336 10337 10338 10339 10340 10341 10342 10343 10344 10345 10346
## [2473] 10347 10348 10349 10350 10351 10352 10353 10354 10355 10356 10357 10358
## [2485] 10359 10360 10361 10362 10363 10364 10365 10366 10367 10368 10369 10370
## [2497] 10371 10372 10373 10374 10375 10376 10377 10378 10379 10380 10381 10382
## [2509] 10383 10384 10385 10386 10387 10388 10389 10390 10391 10392 10393 10394
## [2521] 10395 10396 10397 10398 10399 10400 10401 10402 10403 10404 10405 10406
## [2533] 10407 10408 10409 10410 10411 10412 10413 10414 10415 10416 10417 10418
## [2545] 10419 10420 10421 10422 10423 10424 10425 10426 10427 10428 10429 10430
## [2557] 10431 10432 10433 10434 10435 10436 10437 10438 10439 10440 10441 10442
## [2569] 10443 10444 10445 10446 10447 10448 10449 10450 10451 10452 10453 10454
## [2581] 10455 10456 10457 10458 10459 10460 10461 10462 10463 10464 10465 10466
## [2593] 10467 10468 10469 10470 10471 10472 10473 10474 10475 10476 10477 10478
## [2605] 10479 10480 10481 10482 10483 10484 10485 10486 10487 10488 10489 10490
## [2617] 10491 10492 10493 10494 10495 10496 10497 10498 10499 10500 10501 10502
## [2629] 10503 10504 10505 10506 10507 10508 10509 10510 10511 10512 10513 10514
## [2641] 10515 10516 10517 10518 10519 10520 10521 10522 10523 10524 10525 10526
## [2653] 10527 10528 10529 10530 10531 10532 10533 10534 10535 10536 10537 10538
## [2665] 10539 10540 10541 10542 10543 10544 10545 10546 10547 10548 10549 10550
## [2677] 10551 10552 10553 10554 10555 10556 10557 10558 10559 10560 10561 10562
## [2689] 10563 10564 10565 10566 10567 10568 10569 10570 10571 10572 10573 10574
## [2701] 10575 10576 10577 10578 10579 10580 10581 10582 10583 10584 10585 10586
## [2713] 10587 10588 10589 10590 10591 10592 10593 10594 10595 10596 10597 10598
## [2725] 10599 10600 10601 10602 10603 10604 10605 10606 10607 10608 10609 10610
## [2737] 10611 10612 10613 10614 10615 10616 10617 10618 10619 10620 10621 10622
## [2749] 10623 10624 10625 10626 10627 10628 10629 10630 10631 10632 10633 10634
## [2761] 10635 10636 10637 10638 10639 10640 10641 10642 10643 10644 10645 10646
## [2773] 10647 10648 10649 10650 10651 10652 10653 10654 10655 10656 10657 10658
## [2785] 10659 10660 10661 10662 10663 10664 10665 10666 10667 10668 10669 10670
## [2797] 10671 10672 10673 10674 10675 10676 10677 10678 10679 10680 10681 10682
## [2809] 10683 10684 10685 10686 10687 10688 10689 10690 10691 10692 10693 10694
## [2821] 10695 10696 10697 10698 10699 10700 10701 10702 10703 10704 10705 10706
## [2833] 10707 10708 10709 10710 10711 10712 10713 10714 10715 10716 10717 10718
## [2845] 10719 10720 10721 10722 10723 10724 10725 10726 10727 10728 10729 10730
## [2857] 10731 10732 10733 10734 10735 10736 10737 10738 10739 10740 10741 10742
## [2869] 10743 10744 10745 10746 10747 10748 10749 10750 10751 10752 10753 10754
## [2881] 10755 10756 10757 10758 10759 10760 10761 10762 10763 10764 10765 10766
## [2893] 10767 10768 10769 10770 10771 10772 10773 10774 10775 10776 10777 10778
## [2905] 10779 10780 10781 10782 10783 10784 10785 10786 10787 10788 10789 10790
## [2917] 10791 10792 10793 10794 10795 10796 10797 10798 10799 10800 10801 10802
## [2929] 10803 10804 10805 10806 10807 10808 10809 10810 10811 10812 10813 10814
## [2941] 10815 10816 10817 10818 10819 10820 10821 10822 10823 10824 10825 10826
## [2953] 10827 10828 10829 10830 10831 10832 10833 10834 10835 10836 10837 10838
## [2965] 10839 10840 10841 10842 10843 10844 10845 10846 10847 10848 10849 10850
## [2977] 10851 10852 10853 10854 10855 10856 10857 10858 10859 10860 10861 10862
## [2989] 10863 10864 10865 10866 10867 10868 10869 10870 10871
chrom2.div.small <- chrom2.div[which(chrom2.div$SNP < 8980),]
chrom2.div.small <- chrom2.div.small[which(chrom2.div.small$SNP > 8600),]
# 39200001 to 51650000
tail(chrom2.div.small)
##      CHROM BIN_START  BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 8974     2  54950001 55000000        888        0.00735755    0.00381462
## 8975     2  55000001 55050000        933        0.00726278    0.00291817
## 8976     2  55050001 55100000        914        0.01519790    0.01316560
## 8977     2  55100001 55150000        953        0.00000000    0.00000000
## 8978     2  55150001 55200000        950        0.02453080    0.01712540
## 8979     2  55200001 55250000       1123        0.04151250    0.03948120
##      WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU      PI_UK
## 8974         0.0276795     0.0190044        0.00474647   0.000776248 0.00628362
## 8975         0.0713227     0.0569079        0.02199250   0.012000000 0.00591970
## 8976         0.0273716     0.0252403        0.02200510   0.021276300 0.00620980
## 8977         0.0000000     0.0000000        0.00000000   0.000000000 0.00641686
## 8978         0.0316370     0.0245065        0.00000000   0.000000000 0.00571444
## 8979         0.0553261     0.0473515        0.00000000   0.000000000 0.00691236
##           PI_US      PI_AU TajimaD_UK TajimaD_US TajimaD_AU  SNP   piUK.piAU
## 8974 0.00569739 0.00600235   0.879169   0.756094   0.883948 8974  0.00028127
## 8975 0.00610279 0.00580834   0.666973   0.503085   0.723701 8975  0.00011136
## 8976 0.00523177 0.00562215   0.725060   0.274965   0.841877 8976  0.00058765
## 8977 0.00635051 0.00638269   0.712378   0.737009   0.851269 8977  0.00003417
## 8978 0.00595467 0.00629788   0.408608   0.485645   0.808132 8978 -0.00058344
## 8979 0.00762392 0.00753313   0.871839   0.848962   0.897227 8979 -0.00062077
##        piUK.piUS
## 8974  0.00058623
## 8975 -0.00018309
## 8976  0.00097803
## 8977  0.00006635
## 8978 -0.00024023
## 8979 -0.00071156
quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1)) # set rows
par(mar=c(0,2,0.5,2)) # set margins for each plot
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.5))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_US, col="#2c81a8", lwd=2)
axis(side=4, ylim=c(0,0.04))
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,2.6))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.6))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.6))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,2.4)) # tajima's D axis
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome2.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 6

chrom6.div <- div[which(div$CHROM==6),]
# runs from 16155 to 16871
# chrom 6 = 716 50kb windows ("SNPs" here)

chrom6.div.small <- chrom6.div[which(chrom6.div$SNP < 16325),]
chrom6.div.small <- chrom6.div.small[which(chrom6.div.small$SNP > 16225),]

head(chrom6.div.small)
##       CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 16226     6   3550001 3600000        570        0.01206770   0.008669440
## 16227     6   3600001 3650000        528        0.03079550   0.023766500
## 16228     6   3650001 3700000        701        0.02597620   0.020965100
## 16229     6   3700001 3750000        805        0.00338468   0.000400509
## 16230     6   3750001 3800000        830        0.02082790   0.017915200
## 16231     6   3800001 3850000        800        0.00589529   0.001372410
##       WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU
## 16226         0.0367649     0.0305788         0.0392762     0.0368076
## 16227         0.0501026     0.0406660         0.0739191     0.0613789
## 16228         0.0711590     0.0593933         0.0454643     0.0368937
## 16229         0.0754966     0.0604820         0.0708642     0.0562380
## 16230         0.0375712     0.0322166         0.0438655     0.0358050
## 16231         0.1001270     0.0672706         0.0743417     0.0579428
##            PI_UK      PI_US      PI_AU TajimaD_UK TajimaD_US TajimaD_AU   SNP
## 16226 0.00366513 0.00384209 0.00372947   0.592114   0.972481   0.785959 16226
## 16227 0.00349155 0.00329177 0.00364804   0.682877   0.782069   0.892366 16227
## 16228 0.00468438 0.00458936 0.00478475   0.859767   0.784092   0.972961 16228
## 16229 0.00535166 0.00537373 0.00535428   0.706416   0.819633   0.873451 16229
## 16230 0.00555363 0.00537467 0.00539254   0.840503   0.753410   0.770190 16230
## 16231 0.00531258 0.00519576 0.00516348   0.695610   0.790285   0.812897 16231
##         piUK.piAU   piUK.piUS
## 16226 -0.00006434 -0.00017696
## 16227 -0.00015649  0.00019978
## 16228 -0.00010037  0.00009502
## 16229 -0.00000262 -0.00002207
## 16230  0.00016109  0.00017896
## 16231  0.00014910  0.00011682
chrom6.div.hifst <- chrom6.div[which(chrom6.div$SNP < 16281),]
chrom6.div.hifst <- chrom6.div.small[which(chrom6.div.small$SNP > 16263),]

# AUUK high fst: window 5350001 to 6300001 on Chrom 6
# "SNP" 16263 to 16281

mean(chrom6.div.hifst$PI_UK) 
## [1] 0.002873224
mean(chrom6.div.hifst$PI_US) 
## [1] 0.002896161
mean(chrom6.div.hifst$PI_AU) 
## [1] 0.00293987
chrom6.div.med <- chrom6.div[which(chrom6.div$SNP < 16450),]
chrom6.div.med <- chrom6.div.med[which(chrom6.div.med$SNP > 16155),]
quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_US, col="#2c81a8", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,2.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome6.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2
quartz(height=3,width=7)
options(scipen=999)
par(new=T)
## Warning in par(new = T): calling par(new=TRUE) with no plot
plot((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.01,0.4))
lines((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.01,0.4))
lines((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.01,0.4))
lines((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.01,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome6.BroaderFSTaroundPeak.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 1

chrom1.div <- div[which(div$CHROM==1),]
chrom1.div.small <- chrom1.div[which(chrom1.div$SNP < 6700),]
chrom1.div.small <- chrom1.div.small[which(chrom1.div.small$SNP > 6400),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome1.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 1A

chrom1A.div <- div[which(div$CHROM==1.25),]
# 2896 to 4342
# 1446 windows, 3 ticks

chrom1A.div.small <- chrom1A.div[which(chrom1A.div$SNP < 4000),]
chrom1A.div.small <- chrom1A.div.small[which(chrom1A.div.small$SNP > 3700),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome1A.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 4

chrom4.div <- div[which(div$CHROM==4),]
# 13506 to 14923,
# 1417 windows, 7 ticks - peak ~500 windows from start 

chrom4.div.small <- chrom4.div[which(chrom4.div$SNP < 14150),]
chrom4.div.small <- chrom4.div.small[which(chrom4.div.small$SNP > 13850),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_UK, col="#39C855", lwd=1)

axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome4.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 4A

chrom4A.div <- div[which(div$CHROM==4.5),]
# 13097 to 13505
# 408 windows, 4 ticks

chrom4A.div.small <- chrom4A.div[which(chrom4A.div$SNP < 13300),]
chrom4A.div.small <- chrom4A.div.small[which(chrom4A.div.small$SNP > 13150),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome4A.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2